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Regional Economic Disparities as Determinants of Students’ Achievement in Italy: Data and variables

The analysis proposed by Invalsi for the year 2007/08 does not test these different hypotheses; nevertheless, in the report about the test in 2008/09 they consider the latter as the most credible. Indeed, in the second national examination (2008/09) the final results have been corrected to take “cheating” into account, through a complex procedure based on four factors (statistical details in Invalsi, 2009): (i) the average of achievement scores at class level, (ii) the variance of achievement scores at class level, (iii) an index about missing answers to the test, (iv) an index of uniformity across students within the same class. Such procedure generates a new set of “corrected” scores, namely those that we use in this paper. Table 1a shows an overview of the variables employed in the study, while table 1b contains the descriptive statistics of the dataset.
As covariates at individual level, we employed several students’ characteristics: gender (dummy: Female), citizenship (a dummy – Foreign – for students who are not Italian), disabled status (dummy: Disabled). Two variables have been added to control for the age of students. A student who is in time for the final examination should be born in 1994; however, some students were enrolled a year before (Early), and some students were not admitted to the next grade during their past academic career, so they are older than regular ones (Late).
Unfortunately, the dataset does not include information on the individual student’s socio-economic status (SES), so we cannot control for this important characteristic. This point is strongly important here, and it must be borne in mind when interpreting the results: much of the variance at individual level is not explained exactly because the lack of this important information. When turning to school-level variables, the source of data is twofold. Part of the variables comes from the same Invalsi dataset (final examination of the lower secondary education, year 2008/09). Another important source was the TIMSS 2007 dataset, which refer to the year 2006/07, as described above. Satisfaction level of teachers

An indicator was originally included to define if the school is public or private (dummy: Private), but it was dropped in the results because the sample includes just 6 private schools (less than 1% of the sample). However, alternative specifications in which this variable was included did not come to different results. The proportion of students coming from disadvantaged families has been included to control for low socio-economic conditions of the students population (disadvantaged), given that the economic literature showed a positive relationship between socio-economic background and performance. Our socio-economic variable takes value 1 if the proportion of students from low socio-economic background is in the range, 2 if, 3 if, and 4 if [>50]. Also, we controlled for the intensity of resource availability, by including the following indicator: “shortage” of instructional materials (short_instr), and it is recorded on a four-tiers scale as follows: (1=none, 2=a little, 3=some, 4=a lot). Moreover, an indicator of the environment in which the school is located was introduced, by including an ordinal variable considering the dimension of the city/town (community): the value is 1 if citizens are [>500,000], 2 if, 3 if, 4 if, 5 if, and 6 if [<3,000].
Finally, we considered differences according to the macro-area in which the school is located. Indeed, previous literature on the achievement of Italian students demonstrated that there are relevant differences across the different areas of the country, with schools located in the Central part of Italy performing worse than those in the North and better than those in the South.

Table 1: Variables’ overview

Variable Description
Variables at individual student level
Female Gender (Female = 1) Binary
Disabled Dummy if the student has a disabled status (=1) Binary
Foreign Student’s nationality (foreign=1) Binary
Early A student who enrolled one year before regular track Binary
Late A student who repeated one or more year Binary
Variables at school level
Disadvantaged Average socio-economic status of students at ith school Categorical
(Proportion of students whose families have economic difficulties) Categorical
Short_Instr Shortage of instructional materials Categorical
Community Environment in which the school is located (big city, city, town, rural area) Categorical
Macroareas Two dummies (Central Italy and Southern Italy) Binary

Table 1b: Descriptive statistics

Panel A. Descriptive statistics (continuous variables)

Variable Mean Std. Dev. Min Max Obs
Math_Score 62.22 21.92 0.00 99.99 21,336

Panel B. Descriptive statistics (binary and categorical variables)

Variable Proportion(%) Min Max Obs
Female 0.49 0.00 1.00 21,336
Disabled 0.00 0.00 1.00 21,343
Foreign 0.07 0.00 1.00 21,336
Early 0.05 0.00 1.00 21,336
Late 0.09 0.00 1.00 21,336
Northern Italy 0.38 0.00 1.00 21,336
Central Italy 0.21 0.00 1.00 21,336
Southern Italy 0.41 0.00 1.00 21,336
Disadvantaged (0-10%) 0.39 0.00 1.00 19,838
Disadvantaged (11-25%) 0.38 0.00 1.00 19,838
Disadvantaged (26-50%) 0.17 0.00 1.00 19,838
Shortage of instructional material (High) 0.02 0.00 1.00 21,343
Shortage of instructional material (Some) 0.12 0.00 1.00 21,343
Community: big city 0.12 0.00 1.00 21,343
Community: city 0.31 0.00 1.00
This post was written by , posted on January 7, 2014 Tuesday at 12:09 pm